A comprehensive architecture guide for production LLM serving. From GPU scheduling and model parallelism to observability and autoscaling — everything you need to deploy open-source LLMs at scale on Kubernetes.
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PagedAttention for near-zero KV cache waste, continuous batching for max GPU utilization, tensor and pipeline parallelism for multi-GPU/multi-node serving. Plus the simple Python API and OpenAI compatibility.
Support for Llama 3.1 (8B–405B), DeepSeek R1/V3 with MoE, Qwen, Mistral, Gemma, Phi, and vision-language models like Pixtral 12B and Qwen2-VL. PyTorch-based hardware abstraction across NVIDIA, AMD, Intel, and Google TPU.
Autoscaling based on queue depth, prefix-cache-affinity routing, zero-downtime rolling updates, disaggregated prefill/decode, and wide expert parallelism for MoE models.
Kubernetes-native CRDs (RayCluster, RayService, RayJob), declarative cluster provisioning, auto-healing, GPU-aware scheduling, and native integration with K8s secrets, ConfigMaps, and PVCs.
Complete RayService YAML manifest, single-node tensor parallelism vs multi-node pipeline parallelism, data parallel replicas, and scaling from 1 GPU to 32+ GPUs across nodes.
Unified Prometheus metrics from vLLM, Ray, and Kubernetes. TTFT, TPOT, KV cache utilization, and GPU metrics in Grafana. Performance comparison vs TGI and TRT-LLM.
We deliver tailored technical presentations on AI infrastructure, LLM serving architecture, and Kubernetes-native ML ops.
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